AI研究需超越方法严谨，融入伦理与责任考量。
Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
- 提出六维严谨性框架，涵盖认知、规范、概念等维度
- 强调研究选题背景与理论建构的清晰性至关重要
- 适合研究人员、政策制定者及媒体从业者参考
在人工智能研究与实践中，严谨性通常被理解为方法论上的严谨性——例如数学、统计或计算方法是否正确应用。我们认为，这种狭隘的严谨性定义导致了负责任AI社区的诸多担忧，包括对AI系统能力的夸大宣称。我们主张需要一种更广泛的严谨性概念。除了扩展方法论严谨性外，还应包含：（1）研究选题所依赖的背景知识（认知严谨性）；（2）学科、社群或个人规范、标准或信念如何影响工作（规范严谨性）；（3）理论构念是否清晰表述（概念严谨性）；（4）报告内容与方式（报告严谨性）；（5）从现有证据推断结论的合理性（解释严谨性）。该框架为研究人员、政策制定者、记者及其他利益相关方提供了对话语言与结构。
原文摘要 · Abstract (English)
In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about the capabilities of AI systems. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception -- in addition to a more expansive understanding of (1) methodological rigor -- should include aspects related to (2) what background knowledge informs what to work on (epistemic rigor); (3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); (4) how clearly articulated the theoretical constructs under use are (conceptual rigor); (5) what is reported and how (reporting rigor); and (6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also provide useful language and a framework for much-needed dialogue about the AI community's work by researchers, policymakers, journalists, and other stakeholders.
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